- Company Name
- Leonar
- Job Title
- AI Engineer - Conseil en Management
- Job Description
-
Job title: AI Engineer – Management Consulting
Role Summary
Drive the end‑to‑end industrialisation of AI solutions for a leading European management consultancy. Transform proof‑of‑concepts into scalable, secure, and compliant production systems used by thousands of end‑users, and continuously innovate on agent and multimodal paradigms.
Expectations
- Transition POCs into reliable, high‑performance production assets.
- Architect and optimise end‑to‑end AI pipelines, from data pre‑processing to fine‑tuning and deployment.
- Ensure rigorous MLOps practices: CI/CD, monitoring, rollback, and compliance with data‑security regulations.
- Act as a subject‑matter expert in generative and predictive AI, driving adoption and business value.
Key Responsibilities
- Design, train, and fine‑tune large‑scale machine‑learning models and autonomous agents.
- Build, maintain, and orchestrate production‑grade pipelines (data ingestion, feature engineering, model serving).
- Deploy models on secure, sovereign infrastructures; implement observability, logging, and automated scaling.
- Collaborate with cross‑functional teams (sales, product, governance) to embed AI into client workflows.
- Conduct research on emerging agentic and multimodal technologies to feed future solution development.
- Mentor junior team members and contribute to the business unit’s knowledge base.
Required Skills
- Proficient in MLOps tooling (Kubeflow, MLflow, Airflow, Terraform, or equivalent).
- Strong programming in Python and experience with deep‑learning frameworks (PyTorch, TensorFlow).
- Expertise in model deployment (Docker, Kubernetes, ONNX, TensorRT) and automated scaling.
- Deep understanding of data‑pipeline construction, feature stores, and data‑quality engineering.
- Knowledge of security, privacy, and compliance standards (GDPR, ISO/IEC 27001).
- Experience with monitoring and observability solutions (Prometheus, Grafana, ELK).
- Familiarity with generative AI (LLMs, vision‑language models) and multimodal frameworks.
- Excellent communication skills to translate technical concepts to business stakeholders.
Required Education & Certifications
- Bachelor’s or Master’s in Computer Science, Machine Learning, Data Engineering, or related technical field, or an equivalent business‑to‑technology qualification.
- Preferred: MSc/PhD in AI/ML or MBA with a strong technical focus.
- Certifications in MLOps (MLOps Certified Practitioner, TensorFlow Developer, or similar) and cloud platforms (AWS, GCP, Azure) are advantageous.